Fuyong Xing

5.1k total citations · 1 hit paper
79 papers, 2.9k citations indexed

About

Fuyong Xing is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Biophysics. According to data from OpenAlex, Fuyong Xing has authored 79 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 40 papers in Computer Vision and Pattern Recognition and 20 papers in Biophysics. Recurrent topics in Fuyong Xing's work include AI in cancer detection (38 papers), Cell Image Analysis Techniques (19 papers) and Radiomics and Machine Learning in Medical Imaging (14 papers). Fuyong Xing is often cited by papers focused on AI in cancer detection (38 papers), Cell Image Analysis Techniques (19 papers) and Radiomics and Machine Learning in Medical Imaging (14 papers). Fuyong Xing collaborates with scholars based in United States, China and Singapore. Fuyong Xing's co-authors include Lin Yang, Yuanpu Xie, Hai Su, Fujun Liu, Xiaoshuang Shi, David J. Foran, Xiangfei Kong, Xin Qi, Zizhao Zhang and Lin Yang and has published in prestigious journals such as Bioinformatics, PLoS ONE and IEEE Transactions on Image Processing.

In The Last Decade

Fuyong Xing

76 papers receiving 2.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

2016 341 citations Fuyong Xing, Lin Yang profile →

Peers

Fuyong Xing

AI

CVPR

RNMI

BIOPH

MT

Daniel Racoceanu France

David J. Foran United States

Amit Sethi India

Ali Can United States

Korsuk Sirinukunwattana United Kingdom

Shan E Ahmed Raza United Kingdom

Ajay Basavanhally United States

Hai Su United States

Md Mamunur Rahaman China

Daniel Racoceanu France

Fuyong Xing

1.8k ×1.1

AI

1.3k ×1.0

CVPR

1.0k ×1.1

RNMI

518 ×0.8

BIOPH

171 ×0.6

MT

Citations per year, relative to Fuyong Xing Fuyong Xing (= 1×) peers Daniel Racoceanu

Countries citing papers authored by Fuyong Xing

Since Specialization

Citations

This map shows the geographic impact of Fuyong Xing's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fuyong Xing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fuyong Xing more than expected).

Fields of papers citing papers by Fuyong Xing

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fuyong Xing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fuyong Xing. The network helps show where Fuyong Xing may publish in the future.

Co-authorship network of co-authors of Fuyong Xing

This figure shows the co-authorship network connecting the top 25 collaborators of Fuyong Xing. A scholar is included among the top collaborators of Fuyong Xing based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fuyong Xing. Fuyong Xing is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Russell, Seth, Peter E. DeWitt, Laura Helmkamp, et al.. (2025). Predicting intracranial pressure monitor placement in children with traumatic brain injury: a prospective cohort study to develop a clinical decision support tool. Journal of the American Medical Informatics Association. 33(1). 182–192.

2.

Liu, Zhigang, et al.. (2024). IoU-guided Siamese network with high-confidence template fusion for visual tracking. Neurocomputing. 614. 128774–128774. 3 indexed citations

3.

Wang, Chenggang, et al.. (2024). Shear capacity analysis of prefabricated concrete column-steel beam joints connected by tie-bars. Journal of Constructional Steel Research. 224. 109138–109138. 1 indexed citations

4.

Zhuang, Yonghua, Fuyong Xing, Debashis Ghosh, et al.. (2023). Deep learning on graphs for multi-omics classification of COPD. PLoS ONE. 18(4). e0284563–e0284563. 16 indexed citations

5.

Wang, Wei, et al.. (2023). Imaging Biomarkers in Young Patients With ADPKD. Kidney International Reports. 8(10). 2153–2155. 1 indexed citations

6.

Xing, Fuyong, et al.. (2023). Location-Aware Encoding for Lesion Detection in $^{68}$Ga-DOTATATE Positron Emission Tomography Images. IEEE Transactions on Biomedical Engineering. 71(1). 247–257. 6 indexed citations

7.

Rickert, Christian, Julia Wrobel, Benjamin G. Bitler, et al.. (2023). A platform-independent framework for phenotyping of multiplex tissue imaging data. PLoS Computational Biology. 19(9). e1011432–e1011432. 3 indexed citations

8.

Xing, Fuyong, et al.. (2023). Learning with limited target data to detect cells in cross-modality images. Medical Image Analysis. 90. 102969–102969. 2 indexed citations

9.

Zhuang, Yonghua, Fuyong Xing, Debashis Ghosh, et al.. (2022). An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning. Frontiers in Genetics. 12. 760299–760299. 5 indexed citations

10.

Xing, Fuyong, et al.. (2022). Learning with Context Encoding for Single-Stage Cranial Bone Labeling and Landmark Localization. Lecture notes in computer science. 13438. 286–296. 5 indexed citations

11.

Xing, Fuyong & Toby C. Cornish. (2022). Low-Resource Adversarial Domain Adaptation for Cross-modality Nucleus Detection. Lecture notes in computer science. 13437. 639–649. 3 indexed citations

12.

Xing, Fuyong, et al.. (2021). Automated liver lesion detection in 68Ga DOTATATE PET/CT using a deep fully convolutional neural network. EJNMMI Research. 11(1). 98–98. 24 indexed citations

13.

Xing, Fuyong, Yuanpu Xie, Xiaoshuang Shi, et al.. (2019). Towards pixel-to-pixel deep nucleus detection in microscopy images. BMC Bioinformatics. 20(1). 472–472. 30 indexed citations

14.

Jensen, Alexandria, et al.. (2019). Kernel machine tests of association between brain networks and phenotypes. PLoS ONE. 14(3). e0199340–e0199340. 3 indexed citations

15.

Shi, Xiaoshuang, Hai Su, Fuyong Xing, et al.. (2019). Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis. Medical Image Analysis. 60. 101624–101624. 41 indexed citations

16.

Xing, Fuyong, Tellen D. Bennett, & Debashis Ghosh. (2019). Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification. Lecture notes in computer science. 11764. 740–749. 16 indexed citations

17.

Shi, Xiaoshuang, et al.. (2017). Supervised graph hashing for histopathology image retrieval and classification. Medical Image Analysis. 42. 117–128. 33 indexed citations

18.

Wang, Hongyuan, Fuyong Xing, Hai Su, Arnold J. Stromberg, & Lin Yang. (2014). Novel image markers for non-small cell lung cancer classification and survival prediction. BMC Bioinformatics. 15(1). 310–310. 50 indexed citations

19.

Xing, Fuyong & Lin Yang. (2013). Robust Selection-Based Sparse Shape Model for Lung Cancer Image Segmentation. Lecture notes in computer science. 16(Pt 3). 404–412. 16 indexed citations

20.

Xing, Fuyong, David J. Foran, Lin Yang, & Xin Qi. (2012). A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology. Methods of Information in Medicine. 51(3). 260–267. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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